A Two-Stage Machine Learning Approach for Calving Detection in Rangeland Cattle

Yuxi Wang, Andrés Perea, Huiping Cao (), Mehmet Bakir and Santiago Utsumi
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Yuxi Wang: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
Andrés Perea: Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA
Huiping Cao: Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
Mehmet Bakir: Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA
Santiago Utsumi: Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA

Agriculture, 2025, vol. 15, issue 13, 1-21

Abstract: Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions.

Keywords: calving detection; anomaly detection; unsupervised model; supervised model; GNSS; accelerometer (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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